Cognitive Impairment and Dementia Data Modelling

Abstract Recently, a huge amount of data is available for clinical research on cognitive diseases. A lot of challenges arise when data from different repositories should be integrated. Since data entities are stored with different names at different levels of granularity, a common data model is needed, providing a unified description of different factors and indicators of cognitive diseases. This paper proposes a common hierarchical data model of patients with cognitive disorders, which keeps the semantics of the data in a human-readable format and accelerates interoperability of clinical datasets. It defines data entities, their attributes and relationships related to diagnosis and treatment. The data model covers four main aspects of the patient’s profile: (1) personal profile; (2) anamnestic profile, including social status, everyday habits, and head trauma history; (3) clinical profile, describing medical investigations and assessments, comorbidities and the most likely diagnose; and (4) treatment profile with prescribed medications. It provides a native vocabulary, improving data availability, saving efforts, accelerating clinical data interoperability and standardizing data to minimize risk of rework and misunderstandings. The data model enables the application of machine learning algorithms by helping scientists to understand the semantics of information through a holistic view of patient.

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Towards a Newborn Screening Common Data Model: The Utah Newborn Screening Data Model

International Journal of Neonatal Screening ◽

10.3390/ijns7040070 ◽

2021 ◽

Vol 7 (4) ◽

pp. 70

Author(s):

David Jones ◽

Jianyin Shao ◽

Heidi Wallis ◽

Cody Johansen ◽

Kim Hart ◽

...

Keyword(s):

Newborn Screening ◽

Data Model ◽

Data Exchange ◽

Healthcare Providers ◽

Common Data Model ◽

Snomed Ct ◽

Electronic Data ◽

Data Interoperability ◽

Screening Programs ◽

Data Elements

As newborn screening programs transition from paper-based data exchange toward automated, electronic methods, significant data exchange challenges must be overcome. This article outlines a data model that maps newborn screening data elements associated with patient demographic information, birthing facilities, laboratories, result reporting, and follow-up care to the LOINC, SNOMED CT, ICD-10-CM, and HL7 healthcare standards. The described framework lays the foundation for the implementation of standardized electronic data exchange across newborn screening programs, leading to greater data interoperability. The use of this model can accelerate the implementation of electronic data exchange between healthcare providers and newborn screening programs, which would ultimately improve health outcomes for all newborns and standardize data exchange across programs.

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Establishing the common oncology data model for the national biobank consortium of Taiwan.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e19283 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e19283-e19283

Author(s):

Yang Yi-Hsin ◽

Li-Tzong Chen ◽

Shiu-Feng Huang

Keyword(s):

National Health ◽

Real World ◽

Data Model ◽

Data Science ◽

Common Data Model ◽

Data Interoperability ◽

Cancer Data ◽

Common Definition ◽

The Common ◽

Data Elements

e19283 Background: Taiwan has 32 biobanks under Government’ governance. The Ministry of Health and Welfare have established a National Biobank Consortium of Taiwan to unify the specimen quality and the medical record database. The total recruited participants exceed 350,000. The National Health Research Institutes in Taiwan hold the responsibility of establish a common data model for aggregating data elements from electronic health records (EHRs) of institutes through direct feeds. The goals are to assemble a set of common oncology data elements and to facilitate cancer data interoperability for patient care and research across institutes of Biobank Consortium. Methods: We first conduct a thorough review of available EHR data elements for patient characteristics, diagnosis/staging, treatments, laboratory results, vital signs and outcomes. The data dictionary was organized based on HL7 FHIR and also included data elements from Taiwan Cancer Registry (TCR) and National Health Insurance (NHI) Program, which the common definition has already been established and implemented for years. Data elements suggested by ASCO CancerLinQ and minimal Common Oncology Data Elements (mCODE) are also referenced during planning. The final common model was then reviewed by a panel of experts consisting oncologists as well as data science specialists. Results: There are finally 9 data tables with 281 data elements, in which 248 of them are from the routinely uploaded data elements to government agencies (TCR & NHI) and 33 elements are collected with partial common definition among institutes. There are 164 data elements which are to be collected one observation per case, while 117 elements will be accumulated periodically. Conclusions: A comprehensive understanding of genetics, phenotypes, disease variation as well as treatment responses is crucial to fulfill the needs of real-world studies, which potentially would lead to personalized treatment and drug development. At the first stage of this project, we aim to accumulate available EHR structured data elements and to maintain sufficient cancer data quality. Consequently, the database can provide real-world evidence to promote evidence-based & data-driven cancer care.

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